Till date Internet-of-Things (IoT) has played a part in social networking only to extract and understand the context of the user to make effective updates or provide relevant applications. However, there are no existing solutions that allow users to define relationship between various sensors or provide a platform where applications can be developed to draw such relations based on certain business logic.
Accordingly, an effective and quick analytics system for enabling continuous process improvement by performing analytics on data to support variety of socially derived applications and information networks is desired. More accuracy, for such cases, can be provided by effective mining and analytics of sensor data which can facilitate modeling of underlying relationships and interactions in social network construction. Modeling of large amount of real time data captured by means of sensors to derive understanding about socially interacting elements have the potential to enrich the decision making behavioral pattern of socially interacting elements.
However, an independent social network of sensors, which can be used for reduction of sensor data set for analytics, have not been existing till date. All existing prior arts which attempts to link various sensors through social networks only reflects the ways by which sensor data can contribute to a user's social context like location/activity etc. However, using social network theory and applying it to a sensor network to allow more efficient data mining based on specific use cases is not available.
The purpose of having the reduced data set using social networking structure is to make it efficiently searchable for concluding interesting inferences based on the social and behavioral patterns of the interacting elements sharing familiarity and common interest.
Moreover, the problem of failing sensor networks in relating multiple sensor data effectively which could cause analytics to run only on those sensors which are relevant to that particular instance of the application, needs to be addressed. Also, when the application needs to use multiple distributed sensor data for analytics, it requires a huge set of gathered data from all possible sensors for its effective mining.
In the light of foregoing, there exists a need for a system and method whereby relevant sensors can be connected together in a social network paradigm to constitute a reduced data set for effective analytics which can address ever increasing number of challenges associated with socially-centered applications.